Abstract
This study characterizes reinforcement learning with verifiable rewards in a deployed LLM training environment. It analyzes workload variation, GPU idling, parallelism, data management, and load imbalance, and introduces the PolyTrace benchmark suite for evaluation with realistic workloads.
Type
Publication
arXiv preprint arXiv:2509.25279
